Towards a new Deep Learning Based Approach for the Password Prediction

Although tools for tracking and monitoring illegal networks have been developed for centuries, current methods available at the moment still need continues improvement. This is due to the fact that tracking and monitoring illegal networks in the cyberspace has become increasingly challenging for law enforcement agencies due to sophisticated encryption algorithms and strong passwords. Password predicting approaches will allow investigators to crack passwords used by criminals to protect their data or their communications. Hence, it will help judges to prosecute authors of crimes since they will have at their disposal all evidences needed which are, until now, hidden thanks to strong passwords. In this paper, we introduce a work-in-progress password guessing approach, called PassGuess to guess a missing character in a given password. PassGuess is powered by deep learning and it already predicts passwords with 80% accuracy on the train set and 73% accuracy on the test set for a random missing character in any position for a given password. This model is the very first preliminary work of the PassGuess intended for the purpose of the feasibility study and the authors of this paper are confident that the performance of PassGuess can be improved significantly in the future.